Links tagged “data-science”
26 links, newest first.
towardsdatascience.com
Topic Model Labelling with LLMs: a Python-based method for labeling using the GPT4-o-mini tool
github.com
Notebooks working through pandas one recipe at a time — reading data, selecting, grouping, cleaning — on real-world datasets.
seeing-theory.brown.edu
The first chapter of Seeing Theory, an interactive visual introduction to chance events, expectation and variance.
medium.com
How Netflix grew Jupyter notebooks beyond data science into a company-wide platform for data access, workflow templates and scheduled jobs.
github.com
Platform for sharing data-science research as versioned Jupyter notebooks and markdown posts, with review, tagging and a browsable web index.
github.com
Short guide to the Python 3 features that make the migration worthwhile for data scientists — matrix multiplication operator, pathlib, type hints, f-strings and unicode.
pbpython.com
Explains how pandas' transform() paired with groupby() returns a result aligned to the original rows, unlike aggregation, using percentage-of-total as the example.
tryolabs.com
Round-up of ten Python libraries that stood out in 2017, from Pipenv and PyTorch to Dash, FlashText and Luminoth.
medium.com
Rundown of fifteen Python data-science libraries — NumPy, Pandas, Matplotlib, Scikit-Learn, TensorFlow, NLTK and others — grouped by purpose.
multithreaded.stitchfix.com
Argues that data scientists should own their own ETL and production code end to end, with platform engineers building the abstractions that make that autonomy possible.
datacamp.com
Traces how Project Jupyter split off from IPython in 2014, then maps which features — kernels, magics, notebook conversion — belong to which project today.
deeplearningbook.org
Free online edition of Goodfellow, Bengio and Courville's MIT Press textbook on deep learning, covering applied maths, modern practice and open research directions.
alex.smola.org
Course page for UC Berkeley's 2012 Scalable Machine Learning class, with lectures on distributed systems, data stream algorithms, convex optimization, kernels and graphical models.
rocketdatascience.org
A link list of "definitive guides" and handbooks covering data science, big data, analytics, machine learning and related fields.
matthewrocklin.com
The personal site of Matthew Rocklin, a software engineer in the Python data science ecosystem, collecting his writing on technical projects and open source maintenance.
pandas.pydata.org
A whirlwind tour of pandas for new users, covering Series and DataFrame creation, selection, missing data, grouping, reshaping, time series and plotting.
pandas.pydata.org
The pandas user guide chapter on Series and DataFrame — how to build them, how they align on labels, and how selection and arithmetic behave.
stat.cmu.edu
Course site for Larry Wasserman's CMU statistical machine learning class, with the lecture schedule and notes on regression, classification, clustering, graphical models and causal inference.
dataquest.io
Tutorial building a live Twitter stream consumer in Python with tweepy, storing tweets in a database and running basic sentiment analysis on them.
drivendata.github.io
A Cookiecutter template giving data-science projects a logical, standardised but flexible directory structure for code, data and notebooks.
